Hi PyMC team, and in particular @ricardoV94 @aloctavodia @junpenglao who seem to have worked a lot on the SMC module

I was thrilled to find a SMC sampler implementation in a proper established package.

I was wondering, though: how close is this to SMC Samplers a-la-DelMoral, Doucet, etc?

Indeed I was surprised to see its implementation cite Ching and Chen (2007) and Minson et al. (2013), who do not use the term “SMC samplers”, “ABC SMC”, “IMH”, etc, but instead uses terms like “Transitional MCMC” and “CATMIP” which don’t appear in the code.

I come from a computational Bayesian stat background (did my PhD 15 years ago in adaptive SMC for state-space-models), and I wasn’t familiar with those articles, but much more with the Del Moral, Doucet, Jasra (2006) work on SMC samplers, and generally the bibliography in Chapter 17 of Chopin and Papaspiliopoulos (2020).

To be clear: this is really not “hey, you didn’t cite so and so from my gang” academic attribution call This is rather a genuine “Oh, is that what I already knew? Can I be lazy and recycle my knowledge (and the theoretical guarantees etc), or is there a conceptual gap here?”. I didn’t know that other branch of research on that topic, over there in engineering and geophysics journal, and now wondering how much similar your code is to what I already know and trust, vs needing to dive into two papers I didn’t know.

Thanks for any clarification!

References:

Ching, Jianye, and Yi-Chu Chen. 2007. ‘Transitional Markov Chain Monte Carlo Method for Bayesian Model Updating, Model Class Selection, and Model Averaging’. *Journal of Engineering Mechanics* 133 (7): 816–32. https://doi.org/10.1061/(ASCE)0733-9399(2007)133:7(816).

Chopin, Nicolas, and Omiros Papaspiliopoulos. 2020. ‘SMC Samplers’. In *An Introduction to Sequential Monte Carlo*, edited by Nicolas Chopin and Omiros Papaspiliopoulos, 329–55. Springer Series in Statistics. Cham: Springer International Publishing. SMC Samplers | SpringerLink.

Del Moral, Pierre, Arnaud Doucet, and Ajay Jasra. 2006. ‘Sequential Monte Carlo Samplers’. *Journal of the Royal Statistical Society. Series B (Statistical Methodology)* 68 (3): 411–36.

Minson, S. E., M. Simons, and J. L. Beck. 2013. ‘Bayesian Inversion for Finite Fault Earthquake Source Models I—Theory and Algorithm’. *Geophysical Journal International* 194 (3): 1701–26. Bayesian inversion for finite fault earthquake source models I—theory and algorithm | Geophysical Journal International | Oxford Academic.